Summary of Knowledge Mechanisms in Large Language Models: a Survey and Perspective, by Mengru Wang et al.
Knowledge Mechanisms in Large Language Models: A Survey and Perspective
by Mengru Wang, Yunzhi Yao, Ziwen Xu, Shuofei Qiao, Shumin Deng, Peng Wang, Xiang Chen, Jia-Chen Gu, Yong Jiang, Pengjun Xie, Fei Huang, Huajun Chen, Ningyu Zhang
First submitted to arxiv on: 22 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper reviews knowledge mechanism analysis in Large Language Models (LLMs), a crucial step towards trustworthy Artificial General Intelligence (AGI). The authors propose a novel taxonomy, including knowledge utilization and evolution. Knowledge utilization explores how LLMs memorize, comprehend, apply, create, and evolve knowledge over time. The study also delves into the reasons behind the fragility of parametric knowledge and the potential dark knowledge that may be challenging to address. By understanding how LLMs learn and process knowledge, this research aims to provide insights for future advancements in the field. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how Large Language Models (LLMs) work with knowledge. It’s like trying to understand how your brain works when you’re learning something new! The authors make a special list of categories to study how LLMs learn, remember, and use information. They also talk about why some knowledge is fragile, or easily forgotten. By studying how LLMs process knowledge, scientists hope to create more trustworthy artificial intelligence in the future. |
